Biodiesels have gained much popularity because they are cleaner alternative fuels and they can be used directly in diesel engines without modifications. In this paper, a brief review of the key studies pertaining to the engine performance and exhaust emission characteristics of diesel engines fueled with biodiesel blends, exhaust aftertreatment systems, and low-temperature combustion technology is presented. In general, most biodiesel blends result in a significant decrease in carbon monoxide and total unburned hydrocarbon emissions. There is also a decrease in carbon monoxide, nitrogen oxide, and total unburned hydrocarbon emissions while the engine performance increases for diesel engines fueled with biodiesels blended with nano-additives. The development of automotive technologies, such as exhaust gas recirculation systems and low-temperature combustion technology, also improves the thermal efficiency of diesel engines and reduces nitrogen oxide and particulate matter emissions.
In today's era, the world economy needs to move towards a green transformation. Green total factor productivity provides the judgment about a country or region's ability to achieve long-term sustainable development goals. However, many factors considerably affect green total factor productivity that needs to be explored and clarified. This panel study investigates the link between technological input, environmental policies, governmental involvement, manufacturing and logistics industry cooperation, renewable energy consumption, and green total factor productivity in the context of Chinese's manufacturing and logistics industry. Hypotheses are tested through fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) econometric technique. The study used 12 cities data mainly taken from China Urban Statistical Yearbook (2005-2019) and National Economic and Social Development Statistics Bulletin. The results indicate that technological input, environmental policies, governmental involvement, manufacturing and logistics industry cooperation, and renewable energy consumption are significantly linked to green total factor productivity. The result also implies that the factors mentioned above have a crucial role in the transformation process. Moreover, the current research results will help popularize green total factor productivity and provide a new starting point for reducing non-renewable energy consumption and environmental pollution.
The utilization of natural zeolite (NZ) as an adsorbent for NH4+ removal was investigated. Three types of NZ (i.e., NZ01, NZ02, and NZ03) were characterized, and their NH4+ adsorption process in aqueous solution was evaluated. The effect of pH towards NH4+ adsorption showed that the NZ01 has the highest NH4+ adsorption capacity compared with other natural zeolites used. The application of NZ01 for a simultaneous removal of NH4+ and turbidity in synthetic NH4+-kaolin suspension by adsorptive coagulation process for treating drinking water was studied. The addition of NZ01 into the system increased the NH4+ removal efficiency (ηNH4+) from 11.64% without NZ01 to 41.86% with the addition of 0.2 g L-1 of NZ01. The turbidity removal (ηT), however, was insignificantly affected since the ηT was already higher than 98.0% over all studied parameter's ranges. The thermodynamic and kinetic data analyses suggested that the removal of NH4+ obeyed the Temkin isotherm model and pseudo-second-order kinetic model, respectively. Generally, the turbidity removal was due to the flocculation of destabilized solid particles by alum in the suspension system. The ηNH4+ in surface water was 29.31%, which is lower compared with the removal in the synthetic NH4+-kaolin suspension, but a high ηT (98.65%) was observed. It was found that the addition of the NZ01 could enhance the removal of NH4+ as well as other pollutants in the surface water.
Air surface temperature (AST) is a crucial importance element for many applications such as hydrology, agriculture, and climate change studies. The aim of this study is to develop regression equation for calculating AST and to analyze and investigate the effects of atmospheric parameters (O3, CH4, CO, H2Ovapor, and outgoing longwave radiation (OLR)) on the AST value in Iraq. Dataset retrieved from the Atmospheric Infrared Sounder (AIRS) at EOS Aqua Satellite, spanning the years of 2003 to 2016, and multiple linear regression were used to achieve the objectives of the study. For the study period, the five atmospheric parameters were highly correlated (R, 0.855-0.958) with predicted AST. Statistical analyses in terms of β showed that OLR (0.310 to 1.053) contributes significantly in enhancing AST values. Comparisons among selected five stations (Mosul, Kanaqin, Rutba, Baghdad, and Basra) for the year 2010 showed a close agreement between the predicted and observed AST from AIRS, with values ranging from 0.9 to 1.5 K and for ground stations data, within 0.9 to 2.6 K. To make more complete analysis, also, comparison between predicted and observed AST from AIRS for four selected month in 2016 (January, April, July, and October) has been carried out. The result showed a high correlation coefficient (R, 0.87 and 0.95) with less variability (RMSE ≤ 1.9) for all months studied, indicating model's capability and accuracy. In general, the results indicate the advantage of using the AIRS data and the regression analysis to investigate the impact of the atmospheric parameters on AST over the study area.
Rapid progress of industrial development, urbanization and traffic has caused air quality reduction that negatively affects human health and environmental sustainability, especially among developed countries. Numerous studies on the development of air quality forecasting model using machine learning have been conducted to control air pollution. As such, there are significant numbers of reviews on the application of machine learning in air quality forecasting. Shallow architectures of machine learning exhibit several limitations and yield lower forecasting accuracy than deep learning architecture. Deep learning is a new technology in computational intelligence; thus, its application in air quality forecasting is still limited. This study aims to investigate the deep learning applications in time series air quality forecasting. Owing to this, literature search is conducted thoroughly from all scientific databases to avoid unnecessary clutter. This study summarizes and discusses different types of deep learning algorithms applied in air quality forecasting, including the theoretical backgrounds, hyperparameters, applications and limitations. Hybrid deep learning with data decomposition, optimization algorithm and spatiotemporal models are also presented to highlight those techniques' effectiveness in tackling the drawbacks of individual deep learning models. It is clearly stated that hybrid deep learning was able to forecast future air quality with higher accuracy than individual models. At the end of the study, some possible research directions are suggested for future model development. The main objective of this review study is to provide a comprehensive literature summary of deep learning applications in time series air quality forecasting that may benefit interested researchers for subsequent research.
Economic losses due to health-related implications of air pollution were huge and incurred significant burdens towards healthcare providers. The objective of this study is to systematically review published literature on the financial implications of air pollution on health in Asia. Four databases: PubMed, Scopus, NHS Economic Evaluation Database (NHS EED), and Web of Science (WoS) were used to identify all the relevant articles. It was limited to all articles that had been published in the respected databases from January 2007 until March 2017. Twenty-four articles were included in this review. Five of the 24 studies (20.8%) reported financial implications of air pollution-related disease through value of statistical life (VOSL) which ranged from USD180 million to USD2.2 billion, six (25%) studies used cost of illness (COI) to evaluate air pollution-related morbidity and found that the cost ranged from USD5.4 million to USD9.1 billion. Another six studies (25%) used a combination of VOSL and COI for both mortality and morbidity valuation and found that the financial implications ranging from USD253 million to USD2.9 billion. Thirteen (54.2%) studies reported healthcare cost associated with both hospital admission and outpatient visit, five (20.1%) on hospital admission only, and one (4.2%) on outpatient visit only. Economic impacts of air pollution can be huge with significant deterioration of health among the Asians.
Validity of the environmental Kuznets curve (EKC) hypothesis is consistently and widely debated among economists and environmentalists alike throughout time. In Malaysia, transport is one of the "dirtiest" sectors; it intensively consumes energy in powering engines by using fossil fuels and poses significant threats to environmental quality. Therefore, this study attempted an examination into the impact of corruption on transport carbon dioxide (CO2) emissions. By adopting the fully modified ordinary least squares, canonical cointegrating regression, and dynamic ordinary least squares in performing long-run estimations, the results obtained based on the annual data spanning from 1990 to 2017 yielded various notable findings. First, more corruption would be attributable towards increased transport CO2 emissions. Second, a monotonic increment of transport CO2 emission was seen with higher economic growth and thus invalidated the presence of EKC. Overall, this study suggests that Malaysia has yet to reach the level of economic growth synonymous with transport CO2 emission reduction due to the lack of high technology usage in the current system implemented. Therefore, this study could position policy recommendations of use to the Malaysian authorities in designing the appropriate economic and environmental policies, particularly for the transport sector.
Recent years have seen a burgeoning interest in the involvement of corporate sustainability (CS) in sustainable development (SD), yet both concepts are fewer and newer in the academic field. This study aims to present a thorough bibliometric analysis that provides fresh new insights on the subject. Using VOSviewer software, this study analyzed and visualized 1214 documents for the period 2005-2021 in the Web of Science (WoS) database. The findings of the study indicate that the co-theme CS and SD are lasting but thriving research subjects. The findings also revealed that authors from the USA published the highest number of articles followed by the UK, Spain, Italy, and Germany. The co-authorship network showed strong links between these countries. Three main clusters are identified based on research titles frequently co-cited and have significant co-citation connections. In general, this study provides valuable insight into the current status and future trends for research on SDGs. The outcome of the study could be useful to policymakers, regulators, sustainability practitioners, and researchers in understanding the key research areas, influential authors, institutions, and countries actively involved in the field. Considering the involvement of corporate sustainability in sustainable development is a useful and informative endeavor for all community actors. To the best of the authors' knowledge, this is the first comprehensive study that presents the holistic picture of CS and SD-related research to identify the knowledge map between the two subjects.
This study aimed to assess the dynamic simulation models provided by Aspen adsorption (ASPAD) and artificial neural network (ANN) in understanding the adsorption behavior of atenolol (ATN) on gasified Glyricidia sepium woodchips activated carbon (GGSWAC) within fixed bed columns for wastewater treatment. The findings demonstrated that increasing the bed height from 1 to 3 cm extended breakthrough and exhaustion times while enhancing adsorption capacity. Conversely, higher initial ATN concentrations resulted in shorter breakthrough and exhaustion times but increased adsorption capacity. Elevated influent flow rates reduced breakthrough and exhaustion times while maintaining constant adsorption capacity. The ASPAD software demonstrated competence in accurately modeling the crucial exhaustion points. However, there is room for enhancement in forecasting breakthrough times, as it exhibited deviations ranging from 6.52 to 239.53% when compared to the actual experimental data. ANN models in both MATLAB and Python demonstrated precise predictive abilities, with the Python model (R2 = 0.985) outperforming the MATLAB model (R2 = 0.9691). The Python ANN also exhibited superior fitting performance with lower MSE and MAE. The most influential factor was the initial ATN concentration (28.96%), followed by bed height (26.39%), influent flow rate (22.43%), and total effluent time (22.22%). The findings of this study offer an extensive comprehension of breakthrough patterns and enable accurate forecasts of column performance.
Renewable energy systems are vital for a sustainable future, where solid-state hydrogen storage can play a crucial role. Perovskite hydride materials have attracted the scientific community for hydrogen storage applications. The current work focuses on the theoretical study using density functional theory (DFT) to evaluate the characteristics of MgXH3 (X = Co, Cu, Ni) hydrides. The structural, vibrational, electronic, mechanical, thermodynamic, and hydrogen storage properties of these hydrides were investigated. The equilibrium lattice parameters were calculated using the Birch-Murnaghan equation of state-to-energy volume curves. The elastic constants (Cij) and relevant parameters, such as Born criteria, were calculated to confirm the mechanical stability of the hydrides. The Cauchy pressure (Cp) revealed brittle or ductile behavior. The outcomes of the Pugh ratio, Poisson ratio, and anisotropy were also calculated and discussed. The absence of negative lattice vibrational frequencies in phonon dispersion confirmed the lattice's dynamic stability. The heat capacity curves of thermodynamic properties revealed that hydrides can conduct thermal energy. The metallic character and ample interatomic distances of hydrides were confirmed by the band structure and population analysis, which confirmed that hydrides can conduct electrical energy and adsorb hydrogen. The density of state (DOS) and partial DOS unveiled the role of specific atoms in the DOS of the crystal. The calculated gravimetric hydrogen storage capacity of MgCoH3, MgCuH3, and MgNiH3 hydrides was 3.64, 3.32, and 3.49wt%, respectively. Our results provide a deeper understanding of its potential for hydrogen storage applications through a detailed analysis of MgXH3 (X = Co, Cu, Ni) perovskite hydride material.
The seed extract of Abelmoschus esculentus (AE), also known as Okra, was used as a source of reducing and capping agents to synthesized biogenic titanium dioxide nanoparticles (TiO2 NPs) due to its rich flavonoid contents. The synthesized AE-TiO2 nanoparticles were further evaluated by the effect of loading of TiO2 NPs and irradiation time on the photocatalytic degradation of methylene blue dye. The synthesized TiO2 NPs were then characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), thermogravimetric analysis (TGA), energy dispersive X-ray spectroscopy (EDS), Fourier transformed infrared (FTIR) spectroscopy, Raman spectra, UV-visible spectrophotometry, and particle size distribution (PSD). The findings confirmed the successful synthesis of the spherical anatase phase of TiO2 NPs, as well as the existence of phytochemicals in the extract, which were involved in the capping/stabilization of NPs. The synthesized TiO2 NPs were found to be 60-120 nm in size and almost uniformly distributed throughout the sample. The photocatalytic activity measured in a 300 mL cylindrical photochemical reactor and irradiated with 250 watts UV lamp was investigated based on methylene blue degradation. Effects of irradiation time and catalyst loading were elucidated and correlated with the characteristics of the catalysts. The findings revealed that the synthesized TiO2 NPs were well-dispersed, stable, and could achieve more than 80 % degradation in 240 min of irradiation with 90 mg/L of AE-TiO2 NPs loading compared to only 70 % by the commercial one. These results suggested that AE-TiO2 NPs possesses significant catalytic activity, and the photocatalytic process could be used to degrade, decolorize, and mineralize the methylene blue dye. The polyphenolic tannins present in the extract were the reason behind the desirable characteristics of the nanoparticles and better photocatalytic activity of AE-TiO2 NPs.
Herein, we report a detailed study on creating heterojunction between graphitic carbon nitride (g-C3N4) and bismuth phosphate (BiPO4), enhancing the unpaired free electron mobility. This leads to an accelerated photocatalysis of 2,4-dichlorophenols (2,4-DCPs) under sunlight irradiation. The heterojunction formation was efficaciously conducted via a modest thermal deposition technique. The function of g-C3N4 plays a significant role in generating free electrons under sunlight irradiation. Together, the generated electrons at the g-C3N4 conduction band (CB) are transferred and trapped by the BiPO4 to form active superoxide anion radicals (•O2-). These active radicals will be accountable for the photodegradation of 2,4-DCPs. The synthesized composite characteristics were methodically examined through several chemical and physical studies. Due to the inimitable features of both g-C3N4 and BiPO4, its heterojunction formation, 2.5wt% BiPO4/g-C3N4 achieved complete 2,4-DCP removal (100%) in 90 min under sunlight irradiation. This is due to the presence of g-C3N4 that enhanced electron mobility through the formation of heterojunctions that lengthens the electron-hole pairs' lifetime and maximizes the entire solar spectrum absorption to generate active electrons at the g-C3N4 conduction band. Thus, this formation significantly draws the attention for future environmental remediation, especially in enhancing the entire solar spectrum's harvesting.
The Sub-Saharan Africa (SSA) is far lag behind the sustainable targets that set out in the United Nation's Sustainable Development Goals (SDGs), which is highly needed to embark the priorities by their member countries to devise sustainable policies for accessing clean technologies, energy demand, finance, and food production to mitigate high-mass carbon emissions and conserve environmental agenda in the national policy agenda. The study evaluated United Nation's SDGs for environmental conservation and emission reduction in the panel of 35 selected SSA countries, during a period of 1995-2016. The study further analyzed the variable's relationship in inter-temporal forecasting framework for the next 10 years' time period, i.e., 2017-2026. The parameter estimates for the two models, i.e., CO2 model and PM2.5 models are analyzed by Generalized Method of Moment (GMM) estimator that handle possible endogeneity issue from the given models. The results rejected the inverted U-shaped Environmental Kuznets Curve (EKC) for CO2 emissions, while it supported for PM2.5 emissions with a turning point of US$5540 GDP per capita in constant 2010 US$. The results supported the "pollution haven hypothesis" for CO2 emissions, while this hypothesis is not verified for PM2.5 emissions. The major detrimental factors are technologies, FDI inflows, and food deficit that largely increase carbon emissions in a panel of SSA countries. The IPAT hypothesis is not verified in both the emissions; however, population density will largely influenced CO2 emissions in the next 10 years' time period. The PM2.5 emissions will largely be influenced by high per capita income, followed by trade openness, and technologies, over a time horizon. Thus, the United Nation's sustainable development agenda is highly influenced by socio-economic and environmental factors that need sound action plans by their member countries to coordinate and collaborate with each other and work for Africa's green growth agenda.
Removal of oil spillage from the environment is a global concern. Various methods, including the use of fibers as sorbents, have been developed for oil spill control. Oil palm empty fruit bunch (OPEFB) fiber is a plant biomass that may be acetylated by acetic anhydride using N-bromosuccinimide (NBS) as a catalyst; here, the extent of acetylation may be calculated in terms of weight percent gain (WPG). The modified fiber was used to remove Tapis and Arabian crude oils. The optimum time, temperature, and catalyst concentration were 4 h, 120 °C, and 3 %, respectively, and these parameters could achieve an 11.49 % increase in WPG. The optimized parameters improved the adsorption capacity of OPEFB fibers for crude oil removal. The acetylated OPEFB fibers were characterized by using Fourier transform infrared spectroscopy and field emission scanning electron microscopy to observe the functional groups available and morphology. Kinetic and isotherm studies were conducted using different contact times and oil/water ratios. The rate of oil sorption onto the OPEFB fibers can be adequately described by the pseudo-second-order equation. Adsorption studies revealed that adsorption of crude oil on treated OPEFB fiber could be best described by the Langmuir isotherm model.
The pro-poor growth and environmental sustainability are the twin agendas widely discussed in environmental science literature. The technology-embodied growth helps to attain both agendas through knowledge sharing and technology transfer, which trickle down to the poor income group and improve their living standards. Hence, the role of information and communication technologies (ICTs) is deemed crucial in boosting economic growth and is under deep consideration to establish its role in reducing poverty and environmental pollution. The current study examines the long-run relationship between ICTs, poverty reduction, and ecological degradation in Pakistan using time series data from 1975-2018. The short- and long-run parameter estimates were obtained through the Autoregressive Distributed Lag (ARDL) model for robust inferences. The results substantiate the inverted U-shaped Environmental Kuznets Curve relationship between income and emissions with a turning point at US$1000 in the short-run and US$800 in the long-run. The results confirmed the decisive intervention of ICTs factors in the poverty reduction, i.e., computer communications and mobile-telephone-broadband subscriptions support to reduce poverty incidence with the mediation of inbound FDI in a country. As far as income inequality is concerned, it shows that computer services support minimizing income inequality via a channel of high-technology exports in a country. The technology embodied emissions verified in the long-run, where mobile-telephone-broadband subscriptions increase carbon emissions. Finally, mobile-telephone-broadband subscriptions and inbound FDI both are significant contributors to amplify the country's economic growth. The results conclude that poverty reduction and environmental sustainability agenda are achieved by developing green ICT infrastructure in a country.
Averrhoa carambola is a species of tree native to tropical Southeast Asia. It possesses antioxidant and anti-hyperlipidemia effects and has traditionally been used to treat a variety of ailments. However, the presence of oxalic acid in its fruits might restrict its consumption by individuals suffering from kidney disease, and caramboxin can cause neurotoxicity. In this study, we evaluated the acute and sub-chronic toxicity of the methanolic extract of A. carambola leaves (MEAC) in male and female rats. In the acute study, female rats were given a single oral dose of 5000 mg/kg of MEAC and closely examined for distinct indications of toxic effects during the first 4 h, periodically for 48 h, and daily thereafter for 14 days. Rats of both sexes were employed in the sub-chronic investigation for the 28-day repeated dose oral toxicity study. Results of the acute study revealed the safety of MEAC up to a dose of 5000 mg/kg where the rats did not show changes or signs of toxicity. In the sub-chronic toxicity study, MEAC (250, 500, and 1000 mg/kg) administration did not affect the body weight, food, and water consumption, motor coordination, behavior, or mental alertness in the treated rats. In addition, no variations in hematological or biochemical markers were found in MEAC-treated rats. In conclusion, these findings pinpoint the safety of MEAC at doses up to 5000 mg/kg. The leaves of A. carambola could be safely consumed by people with kidney disease to treat other ailments.
Protein hydrolysates and bioactive peptides from various protein sources have demonstrated their effectiveness for the prevention of illness and the improvement of symptoms from several diseases. In particular, the use of microalgae to generate bioactive peptides has received a growing interest because of their potential to be cultivated on non-arable land and high nutritional value. However, scant research is available on the toxicity of peptide-based preparations. The present study aims to evaluate the toxicity of microalgal protein hydrolysates (MPH) from one marine species of microalgae (Bellerochea malleus) to determine the feasibility of their use for functional food applications. Results showed that the oral administration of MPH at three doses (D1, 100 mg kg-1 BW; D2, 400 mg kg-1 BW; and D3, 2000 mg kg-1 BW) to male Wistar rats did not induce any adverse effects or mortality up to13 days of treatment. Data analysis of relative organ weights and biochemical and hematological parameters did not show any significant differences between control and treated groups at the three doses investigated. Data from histopathological observations did not reveal any signs of major toxicity at the doses D1 and D2. However, mild signs of inflammation and necrosis were observed in the kidney of rats fed MPH at D3. All together, these results reveal the overall safety of MPH and provide new evidence for advocating their use for functional food or nutraceutical applications.
This study aims to gain more understanding on highland farmers' adaptation towards the impacts of climate change in Malaysia. Via a multi-stage cluster sampling, this quantitative study has surveyed a total of 400 highland farmers as respondents. The results indicated that the highest climate change-resilient farmers were from Kundasang, specifically among the females, Dusun ethnic group, and those who work side jobs to cover household expenses. Furthermore, recorded factors such as age and years of experience yielded significant negative relationship with adaptation whereas income yielded significant positive relationship with adaptation. The paper concludes with recommendations related to occupational diversification, consistent information disseminations, access to financial assistance, and the need to empower extension officers and local leaders in the hope that a comprehensive approach can help implement any community climate change-adaptation plan.
This study was conducted to investigate the potential application of oil palm empty fruit branches (OPEFB) as adsorbents to remove organic methylmercurry, MeHg(II), and inorganic Hg(II) from aqueous solution. The OPEFB was functionalized with amine containing ligand namely 3-ureidopropyltriethoxysilane (UPTES) aiming for better adsorption performance towards both mercury ions. The adsorption was found to be dependent on initial pH, initial concentraton, temperatures, and contact time. The maximum adsorption capacities (Qm.exp) of Hg(II) adsorption onto OPEFB and UPTES-OPEFB were 0.226 and 0.773 mmol/g, respectively. The Qm.exp of MeHg(II) onto OPEFB, however, was higher than UPTES-OPEFB. The adsorption kinetic data obeyed the Elovich model and the adsorption was controlled by the film-diffusion step. The calculated thermodynamic parameters indicate an endothermic adsorption process. Adsorption data analysis indicates that the adsorption mechanism may include ion-exchange, complexation, and physisorption interactions. The potential applications of adsorbents were demonstrated using oilfield produced water and natural gas condensate. The UPTES-OPEFB offered higher selectivity towards both mercury ions than OPEFB. The regenerability studies indicated that the adsorbent could be reused for multiple cycles.
Two superior adsorbents, namely bentonite and graphene oxide (GO), were hybridised to study the removal of copper and nickel ions from synthetic and industrial wastewater. The as-synthesised GO, bentonite/GO and bentonite were characterised by Fourier transform infrared spectroscopy, scanning electron microscopy, energy-dispersive X-ray spectroscopy and N2 adsorption-desorption analysis. The factors influencing the adsorption behaviours including contact time, initial solution pH, ionic strength, initial concentration of metal ions, temperature and adsorbent dosage were systematically investigated by batch equilibrium method. The adsorption equilibrium for copper and nickel onto bentonite was attained in 90 min while equilibrium was reached in 60 min on bentonite/GO. The adsorption of copper and nickel was pH-dependent in the range from pH 2 to pH 7 and from pH 2 to pH 8. Pseudo-first-order kinetic model excellently described the adsorption of copper and nickel onto bentonite and bentonite/GO. The equilibrium adsorption data was well described by the Langmuir isotherm model and the maximum adsorption capacity was 248.9 mg/g, 558.4 mg/g, 215.8 mg/g and 402.5 mg/g for bentonite-copper, bentonite/GO-copper, bentonite-nickel and bentonite/GO-nickel adsorption systems, respectively. The bentonite/GO composite exhibited a higher adsorption capacity of both cations from synthetic wastewater than pure bentonite owning to the synergistic effect between bentonite and GO. In all adsorption studies, copper was more efficiently removed than nickel due to its higher tendency to form bond with adsorbent surfaces. The adsorption of copper and nickel on bentonite/GO was mainly due to cation exchange, intermolecular and electrostatic interactions and physisorption dominated the adsorption processes. The practical application of bentonite/GO on adsorption of copper was investigated using real wastewater and its removal efficiency was beyond 98%. The excellent adsorption performances of composites for the copper and nickel removal from wastewater demonstrated its significant potential for pollution mitigations.